Using Graph Attention for Virtual Metrology in Semiconductor Manufacturing (Intel Foundry, ASU)

Researchers from Arizona State University and Intel Foundry have published “Graph Attention-Based Virtual Metrology for Film Deposition Processes in Semiconductor Manufacturing”.

Abstract “Artificial intelligence-driven semiconductor manufacturing increasingly operates at nanometer and angstrom scales, where precise process control depends on accurate and timely metrology. However, physical metrology is limited by measurement latency, cost, and sampling constraints, restricting its scalability in high-volume production. Virtual metrology (VM) has emerged as an effective alternative by predicting wafer-level characteristics from equipment sensor data. Despite recent advances, many existing VM models remain correlation-driven and lack the ability to capture structured dependencies among heterogeneous process variables, while providing limited interpretability. This study presents a graph attention-based VM framework for film deposition processes that integrates temporal feature learning with structured parameter-layer dependency modeling. The proposed approach represents each step-parameter pair as a node and extracts temporal embeddings from high-frequency equipment traces using convolutional feature encoders. A parameter-to-layer graph attention mechanism is employed to model directional dependencies, enabling each film layer to aggregate relevant process information. The framework is evaluated using industrial deposition data collected from production wafers, where the model predicts film thickness from multivariate sensor signals. Experimental results demonstrate improved predictive performance compared to baseline models. In addition, analysis of the learned attention weights reveals interpretable parameter-layer relationships consistent with physical process behavior, capturing dominant process factors and temporal dependencies across deposition stages. These results indicate that the proposed framework enhances prediction accuracy and provides meaningful insight into process dynamics, supporting effective monitoring and optimization in semiconductor manufacturing.”

Find the technical paper here. May 2026.

Han, Tao, Suk Ki Lee, and Hyunwoong Ko. “Graph Attention-Based Virtual Metrology for Film Deposition Processes in Semiconductor Manufacturing.” arXiv:2606.00923, May 2026. https://doi.org/10.48550/arXiv.2606.00923.